Online Voting in Ontario Municipalities: A Standards-Based Review
Bibliographic record
Abstract
Abstract Over two hundred municipalities now offer online voting in Ontario, Canada, representing one of the largest deployments of digital elections worldwide. Many have eliminated the paper ballot altogether. Despite this, no provincial or federal-level standards exist. This gap leaves local election officials to create and apply their own cybersecurity requirements with varying degrees of success. Until a standard can be developed and adopted, we turn to perhaps the most natural and immediate stand-in: The Council of Europe’s (CoE) standards for e-voting. We use this baseline to present the first standards-based analysis of online voting practices in Ontario. Our results find the province is broadly non-compliant, with only 14% of the CoE’s 49 standards and 93 implementation guidelines categorized as fully met. We summarize these differences and identify areas for improvement in the hope of underscoring the need for domestic e-voting standards.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".